Synthesizing information from multiple real-time data sources to generate an intelligent and coherent assessment of a patient's underlying hemodynamic condition is of vital importance for the development of intelligent medical monitors. Dr. Dean Sittig is currently directing the development of a prototype intelligent cardiovascular monitor (ICM). The proposed project builds on this work and includes: 1) identifying medically meaningful trends and artifacts from multiple real-time data sources, 2) generating patient-specific "smart" alarms, and 3) testing and refinement of algorithms designed to recognize several basic hemodynamic abnormalities (e.g., hypovolemia or cardiac tamponade, etc.). This proposed research project will focus particularly on the intelligent synthesis of information derived from multiple real-time data sources (i.e., the trend detection, artifact recognition, and physiological state determination algorithms) to generate a more systematic assessment of a patient's hemodynamic status. Finally, we will test a prototype of the ICM using data from the intensive care unit and the operating room. Trends and artifacts detected, alarms generated, and hemodynamic abnormalities recognized will be compared with a "gold standard" annotated medical record generated by the clinical staff, to help assess the utility of the monitor in terms of the goals listed above.